Foundation Model Engineer

Pure Resourcing Solutions Limited; Cambridgeshire and Peterborough; On-site; GBP 90,000 - 130,000; Full time

Job summary

Pure Resourcing Solutions Limited in Cambridge is recruiting a Foundation Model Engineer to join a fast‑growing AI team. This role offers a competitive salary and benefits on site at their Cambridge base.

You will design end‑to‑end LLM training pipelines, preprocess datasets, fine‑tune open models and build evaluation frameworks across the full AI lifecycle, with emphasis on depth and rigour.

Qualifications

• Experience training and fine-tuning LLMs or multimodal models.
• Strong understanding of model evaluation, bias/variance, and data quality.
• Proficiency in Python and ML frameworks (PyTorch or TensorFlow).

Responsibilities

• Design end-to-end LLM training pipelines.
• Source and preprocess datasets for training and evaluation.
• Fine-tune and optimise open weight models (LLMs, vision or traditional ML).
• Build evaluation frameworks and define performance metrics.
• Develop and maintain data pipelines and training workflows.
• Analyse training pipelines and optimise for latency, cost and scalability.
• Implement monitoring, logging and feedback loops for continuous improvement.
• Experiment with modern AI tooling and services to assess their potential.

Skills

LLM training

Fine-tuning

Python

PyTorch

TensorFlow

ML pipelines

GPU optimisation

Model evaluation

Education

Maths or Computer Science background

Tools

CI/CD for ML

Experiment tracking

Model versioning

Job description

Foundation Model Engineer

Cambridge | Competitive salary + benefits

About the Company

Our client is a well-fund‑ed, mission‑driven organisation working at the forefront of AI development. Led by an experienced team of founders, investors and engineers, they are focused on building safe, responsible AI systems and are growing quickly from a Cambridge base.

The Opportunity

Our client is investing heavily in their machine learning infrastructure and compute capability to accelerate model development and inference. This is a chance to join at an early stage and work across the full AI lifecycle, from experimentation through to scalable deployment, with a strong emphasis on technical depth and rigour.

What You’ll Do

• Design and implement end‑to‑end LLM training pipelines
• Source and, where appropriate, preprocess datasets for training and evaluation
• Fine‑tune and optimise open weight models (LLMs, vision or traditional ML)
• Build evaluation frameworks and define performance metrics
• Develop and maintain data pipelines and training workflows
• Analyse training pipelines and optimise for latency, cost and scalability
• Implement monitoring, logging and feedback loops for continuous improvement
• Experiment with modern AI tooling and services to assess how they can be leveraged

What You’ll Bring

• Proven experience training and fine‑tuning LLMs or multimodal models (not just consuming APIs)
• A solid understanding of model evaluation and validation, overfitting and bias/variance trade‑offs, and data quality and feature engineering
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Experience building and maintaining ML pipelines in production
• Familiarity with GPU usage and optimisation
• The ability to debug and improve model performance systematically

Also Valued

• Knowledge of distributed training or large‑scale data processing
• Experience with MLOps tools (CI/CD for ML, experiment tracking, model versioning)
• A background in applied research or publishing
• Familiarity with retrieval systems, embeddings or ranking models

Ideally you’ll have a maths or computer science research background with a focus on developing new algorithms or techniques for training and deploying AI models, whether gained in a large organisation, a start‑up, or academia, with an emphasis on cutting‑edge machine learning